Overview

Dataset statistics

Number of variables21
Number of observations164750
Missing cells472018
Missing cells (%)13.6%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory87.0 MiB
Average record size in memory553.7 B

Variable types

NUM10
CAT10
BOOL1

Reproduction

Analysis started2023-01-01 00:46:45.619824
Analysis finished2023-01-01 00:47:10.542472
Duration24.92 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
Dataset has 5 (< 0.1%) duplicate rows Duplicates
batsman has a high cardinality: 488 distinct values High cardinality
non_striker has a high cardinality: 484 distinct values High cardinality
bowler has a high cardinality: 378 distinct values High cardinality
player_dismissed has a high cardinality: 464 distinct values High cardinality
fielder has a high cardinality: 476 distinct values High cardinality
total_runs is highly correlated with batsman_runsHigh correlation
batsman_runs is highly correlated with total_runsHigh correlation
player_dismissed has 156593 (95.0%) missing values Missing
dismissal_kind has 156593 (95.0%) missing values Missing
fielder has 158832 (96.4%) missing values Missing
bye_runs is highly skewed (γ1 = 30.0515634) Skewed
noball_runs is highly skewed (γ1 = 24.97400437) Skewed
wide_runs has 159739 (97.0%) zeros Zeros
bye_runs has 164302 (99.7%) zeros Zeros
legbye_runs has 161989 (98.3%) zeros Zeros
noball_runs has 164093 (99.6%) zeros Zeros
batsman_runs has 65904 (40.0%) zeros Zeros
extra_runs has 155872 (94.6%) zeros Zeros
total_runs has 58061 (35.2%) zeros Zeros

Variables

match_id
Real number (ℝ≥0)

Distinct count696
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean977.9517572078908
Minimum1
Maximum7953
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2023-01-01T06:17:10.587944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1175
median349
Q3521
95-th percentile7919
Maximum7953
Range7952
Interquartile range (IQR)346

Descriptive statistics

Standard deviation2147.671843
Coefficient of variation (CV)2.196091808
Kurtosis6.501191032
Mean977.9517572
Median Absolute Deviation (MAD)173
Skewness2.901470981
Sum161117552
Variance4612494.344
2023-01-01T06:17:10.656048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
126 267 0.2%
 
34 263 0.2%
 
476 262 0.2%
 
534 262 0.2%
 
388 261 0.2%
 
190 259 0.2%
 
570 259 0.2%
 
536 258 0.2%
 
401 258 0.2%
 
257 257 0.2%
 
Other values (686) 162144 98.4%
 
ValueCountFrequency (%) 
1 248 0.2%
 
2 247 0.1%
 
3 218 0.1%
 
4 247 0.1%
 
5 248 0.2%
 
ValueCountFrequency (%) 
7953 241 0.1%
 
7952 245 0.1%
 
7951 247 0.1%
 
7950 247 0.1%
 
7949 247 0.1%
 

inning
Categorical

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
85409
2
79260
3
 
43
4
 
38
ValueCountFrequency (%) 
1 85409 51.8%
 
2 79260 48.1%
 
3 43 < 0.1%
 
4 38 < 0.1%
 
2023-01-01T06:17:10.754762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length1
Mean length1
Min length1
ValueCountFrequency (%) 
Decimal_Number 4 100.0%
 
ValueCountFrequency (%) 
Common 4 100.0%
 
ValueCountFrequency (%) 
ASCII 4 100.0%
 

batting_team
Categorical

Distinct count14
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Mumbai Indians
20673
Royal Challengers Bangalore
19300
Kings XI Punjab
19211
Kolkata Knight Riders
19155
Delhi Daredevils
18786
Other values (9)
67625
ValueCountFrequency (%) 
Mumbai Indians 20673 12.5%
 
Royal Challengers Bangalore 19300 11.7%
 
Kings XI Punjab 19211 11.7%
 
Kolkata Knight Riders 19155 11.6%
 
Delhi Daredevils 18786 11.4%
 
Chennai Super Kings 17711 10.8%
 
Rajasthan Royals 15677 9.5%
 
Sunrisers Hyderabad 11132 6.8%
 
Deccan Chargers 9034 5.5%
 
Pune Warriors 5443 3.3%
 
Other values (4) 8628 5.2%
 
2023-01-01T06:17:10.844232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length27
Mean length17.98345372
Min length13
ValueCountFrequency (%) 
Lowercase_Letter 21 56.8%
 
Uppercase_Letter 15 40.5%
 
Space_Separator 1 2.7%
 
ValueCountFrequency (%) 
Latin 36 97.3%
 
Common 1 2.7%
 
ValueCountFrequency (%) 
ASCII 37 100.0%
 

bowling_team
Categorical

Distinct count14
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Mumbai Indians
20573
Royal Challengers Bangalore
19627
Kolkata Knight Riders
19290
Kings XI Punjab
19055
Delhi Daredevils
18725
Other values (9)
67480
ValueCountFrequency (%) 
Mumbai Indians 20573 12.5%
 
Royal Challengers Bangalore 19627 11.9%
 
Kolkata Knight Riders 19290 11.7%
 
Kings XI Punjab 19055 11.6%
 
Delhi Daredevils 18725 11.4%
 
Chennai Super Kings 17533 10.6%
 
Rajasthan Royals 15813 9.6%
 
Sunrisers Hyderabad 10936 6.6%
 
Deccan Chargers 9039 5.5%
 
Pune Warriors 5457 3.3%
 
Other values (4) 8702 5.3%
 
2023-01-01T06:17:10.936138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length27
Mean length18.00811533
Min length13
ValueCountFrequency (%) 
Lowercase_Letter 21 56.8%
 
Uppercase_Letter 15 40.5%
 
Space_Separator 1 2.7%
 
ValueCountFrequency (%) 
Latin 36 97.3%
 
Common 1 2.7%
 
ValueCountFrequency (%) 
ASCII 37 100.0%
 

over
Real number (ℝ≥0)

Distinct count20
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.151878603945372
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2023-01-01T06:17:11.014010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.675665868
Coefficient of variation (CV)0.5590754273
Kurtosis-1.18230573
Mean10.1518786
Median Absolute Deviation (MAD)5
Skewness0.05167105171
Sum1672522
Variance32.21318305
2023-01-01T06:17:11.077877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 8837 5.4%
 
2 8763 5.3%
 
3 8672 5.3%
 
4 8644 5.2%
 
5 8612 5.2%
 
6 8607 5.2%
 
7 8561 5.2%
 
8 8528 5.2%
 
9 8510 5.2%
 
10 8450 5.1%
 
Other values (10) 78566 47.7%
 
ValueCountFrequency (%) 
1 8837 5.4%
 
2 8763 5.3%
 
3 8672 5.3%
 
4 8644 5.2%
 
5 8612 5.2%
 
ValueCountFrequency (%) 
20 6186 3.8%
 
19 7197 4.4%
 
18 7692 4.7%
 
17 7941 4.8%
 
16 8040 4.9%
 

ball
Real number (ℝ≥0)

Distinct count9
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6162427921092566
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2023-01-01T06:17:11.156612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.807397715
Coefficient of variation (CV)0.4997998804
Kurtosis-1.082362006
Mean3.616242792
Median Absolute Deviation (MAD)2
Skewness0.09647601544
Sum595776
Variance3.266686501
2023-01-01T06:17:11.223064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 26715 16.2%
 
2 26640 16.2%
 
3 26567 16.1%
 
4 26504 16.1%
 
5 26419 16.0%
 
6 26328 16.0%
 
7 4728 2.9%
 
8 736 0.4%
 
9 113 0.1%
 
ValueCountFrequency (%) 
1 26715 16.2%
 
2 26640 16.2%
 
3 26567 16.1%
 
4 26504 16.1%
 
5 26419 16.0%
 
ValueCountFrequency (%) 
9 113 0.1%
 
8 736 0.4%
 
7 4728 2.9%
 
6 26328 16.0%
 
5 26419 16.0%
 

batsman
Categorical

HIGH CARDINALITY
Distinct count488
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
V Kohli
 
3879
SK Raina
 
3723
G Gambhir
 
3524
RG Sharma
 
3497
S Dhawan
 
3384
Other values (483)
146743
ValueCountFrequency (%) 
V Kohli 3879 2.4%
 
SK Raina 3723 2.3%
 
G Gambhir 3524 2.1%
 
RG Sharma 3497 2.1%
 
S Dhawan 3384 2.1%
 
RV Uthappa 3234 2.0%
 
MS Dhoni 3001 1.8%
 
AM Rahane 2925 1.8%
 
DA Warner 2902 1.8%
 
CH Gayle 2793 1.7%
 
Other values (478) 131888 80.1%
 
2023-01-01T06:17:11.312423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length20
Mean length9.361317147
Min length5
ValueCountFrequency (%) 
Uppercase_Letter 26 48.1%
 
Lowercase_Letter 26 48.1%
 
Space_Separator 1 1.9%
 
Dash_Punctuation 1 1.9%
 
ValueCountFrequency (%) 
Latin 52 96.3%
 
Common 2 3.7%
 
ValueCountFrequency (%) 
ASCII 54 100.0%
 

non_striker
Categorical

HIGH CARDINALITY
Distinct count484
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
SK Raina
 
3832
G Gambhir
 
3740
V Kohli
 
3705
S Dhawan
 
3650
RG Sharma
 
3540
Other values (479)
146283
ValueCountFrequency (%) 
SK Raina 3832 2.3%
 
G Gambhir 3740 2.3%
 
V Kohli 3705 2.2%
 
S Dhawan 3650 2.2%
 
RG Sharma 3540 2.1%
 
AM Rahane 3192 1.9%
 
RV Uthappa 3125 1.9%
 
CH Gayle 2714 1.6%
 
MS Dhoni 2712 1.6%
 
KD Karthik 2709 1.6%
 
Other values (474) 131831 80.0%
 
2023-01-01T06:17:11.400667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length20
Mean length9.362324734
Min length5
ValueCountFrequency (%) 
Uppercase_Letter 26 48.1%
 
Lowercase_Letter 26 48.1%
 
Space_Separator 1 1.9%
 
Dash_Punctuation 1 1.9%
 
ValueCountFrequency (%) 
Latin 52 96.3%
 
Common 2 3.7%
 
ValueCountFrequency (%) 
ASCII 54 100.0%
 

bowler
Categorical

HIGH CARDINALITY
Distinct count378
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Harbhajan Singh
 
3182
A Mishra
 
2929
PP Chawla
 
2890
SL Malinga
 
2694
R Ashwin
 
2673
Other values (373)
150382
ValueCountFrequency (%) 
Harbhajan Singh 3182 1.9%
 
A Mishra 2929 1.8%
 
PP Chawla 2890 1.8%
 
SL Malinga 2694 1.6%
 
R Ashwin 2673 1.6%
 
P Kumar 2637 1.6%
 
DJ Bravo 2448 1.5%
 
UT Yadav 2366 1.4%
 
B Kumar 2340 1.4%
 
SP Narine 2327 1.4%
 
Other values (368) 138264 83.9%
 
2023-01-01T06:17:11.485791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length17
Mean length9.486179059
Min length5
ValueCountFrequency (%) 
Lowercase_Letter 26 47.3%
 
Uppercase_Letter 24 43.6%
 
Space_Separator 1 1.8%
 
Dash_Punctuation 1 1.8%
 
Open_Punctuation 1 1.8%
 
Decimal_Number 1 1.8%
 
Close_Punctuation 1 1.8%
 
ValueCountFrequency (%) 
Latin 50 90.9%
 
Common 5 9.1%
 
ValueCountFrequency (%) 
ASCII 55 100.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
164669
1
 
81
ValueCountFrequency (%) 
0 164669 > 99.9%
 
1 81 < 0.1%
 

wide_runs
Real number (ℝ≥0)

ZEROS
Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03718361153262519
Minimum0
Maximum5
Zeros159739
Zeros (%)97.0%
Memory size1.3 MiB
2023-01-01T06:17:11.557929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2540868115
Coefficient of variation (CV)6.833301044
Kurtosis190.261922
Mean0.03718361153
Median Absolute Deviation (MAD)0
Skewness11.65335186
Sum6126
Variance0.06456010777
2023-01-01T06:17:11.624839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 159739 97.0%
 
1 4546 2.8%
 
2 219 0.1%
 
5 200 0.1%
 
3 42 < 0.1%
 
4 4 < 0.1%
 
ValueCountFrequency (%) 
0 159739 97.0%
 
1 4546 2.8%
 
2 219 0.1%
 
3 42 < 0.1%
 
4 4 < 0.1%
 
ValueCountFrequency (%) 
5 200 0.1%
 
4 4 < 0.1%
 
3 42 < 0.1%
 
2 219 0.1%
 
1 4546 2.8%
 

bye_runs
Real number (ℝ≥0)

SKEWED
ZEROS
Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004898330804248862
Minimum0
Maximum4
Zeros164302
Zeros (%)99.7%
Memory size1.3 MiB
2023-01-01T06:17:11.729576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1150063737
Coefficient of variation (CV)23.47868658
Kurtosis991.9717646
Mean0.004898330804
Median Absolute Deviation (MAD)0
Skewness30.0515634
Sum807
Variance0.013226466
2023-01-01T06:17:11.801062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 164302 99.7%
 
1 309 0.2%
 
4 109 0.1%
 
2 28 < 0.1%
 
3 2 < 0.1%
 
ValueCountFrequency (%) 
0 164302 99.7%
 
1 309 0.2%
 
2 28 < 0.1%
 
3 2 < 0.1%
 
4 109 0.1%
 
ValueCountFrequency (%) 
4 109 0.1%
 
3 2 < 0.1%
 
2 28 < 0.1%
 
1 309 0.2%
 
0 164302 99.7%
 

legbye_runs
Real number (ℝ≥0)

ZEROS
Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021547799696509863
Minimum0
Maximum5
Zeros161989
Zeros (%)98.3%
Memory size1.3 MiB
2023-01-01T06:17:11.885875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1964100243
Coefficient of variation (CV)9.115084931
Kurtosis237.683774
Mean0.0215477997
Median Absolute Deviation (MAD)0
Skewness13.63127607
Sum3550
Variance0.03857689765
2023-01-01T06:17:11.952614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 161989 98.3%
 
1 2408 1.5%
 
4 204 0.1%
 
2 129 0.1%
 
3 16 < 0.1%
 
5 4 < 0.1%
 
ValueCountFrequency (%) 
0 161989 98.3%
 
1 2408 1.5%
 
2 129 0.1%
 
3 16 < 0.1%
 
4 204 0.1%
 
ValueCountFrequency (%) 
5 4 < 0.1%
 
4 204 0.1%
 
3 16 < 0.1%
 
2 129 0.1%
 
1 2408 1.5%
 

noball_runs
Real number (ℝ≥0)

SKEWED
ZEROS
Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004200303490136571
Minimum0
Maximum5
Zeros164093
Zeros (%)99.6%
Memory size1.3 MiB
2023-01-01T06:17:12.029625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07111078137
Coefficient of variation (CV)16.92991507
Kurtosis1086.84229
Mean0.00420030349
Median Absolute Deviation (MAD)0
Skewness24.97400437
Sum692
Variance0.005056743227
2023-01-01T06:17:12.092621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 164093 99.6%
 
1 641 0.4%
 
2 9 < 0.1%
 
5 6 < 0.1%
 
3 1 < 0.1%
 
ValueCountFrequency (%) 
0 164093 99.6%
 
1 641 0.4%
 
2 9 < 0.1%
 
3 1 < 0.1%
 
5 6 < 0.1%
 
ValueCountFrequency (%) 
5 6 < 0.1%
 
3 1 < 0.1%
 
2 9 < 0.1%
 
1 641 0.4%
 
0 164093 99.6%
 

penalty_runs
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
164748
5
 
2
ValueCountFrequency (%) 
0 164748 > 99.9%
 
5 2 < 0.1%
 
2023-01-01T06:17:12.181474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length1
Mean length1
Min length1
ValueCountFrequency (%) 
Decimal_Number 2 100.0%
 
ValueCountFrequency (%) 
Common 2 100.0%
 
ValueCountFrequency (%) 
ASCII 2 100.0%
 

batsman_runs
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS
Distinct count8
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.237238239757208
Minimum0
Maximum7
Zeros65904
Zeros (%)40.0%
Memory size1.3 MiB
2023-01-01T06:17:12.256055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.60351116
Coefficient of variation (CV)1.296040737
Kurtosis1.65714855
Mean1.23723824
Median Absolute Deviation (MAD)1
Skewness1.588431135
Sum203835
Variance2.57124804
2023-01-01T06:17:12.317132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 65904 40.0%
 
1 61580 37.4%
 
4 18707 11.4%
 
2 10560 6.4%
 
6 7392 4.5%
 
3 543 0.3%
 
5 61 < 0.1%
 
7 3 < 0.1%
 
ValueCountFrequency (%) 
0 65904 40.0%
 
1 61580 37.4%
 
2 10560 6.4%
 
3 543 0.3%
 
4 18707 11.4%
 
ValueCountFrequency (%) 
7 3 < 0.1%
 
6 7392 4.5%
 
5 61 < 0.1%
 
4 18707 11.4%
 
3 543 0.3%
 

extra_runs
Real number (ℝ≥0)

ZEROS
Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0678907435508346
Minimum0
Maximum7
Zeros155872
Zeros (%)94.6%
Memory size1.3 MiB
2023-01-01T06:17:12.392326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3451441709
Coefficient of variation (CV)5.083817805
Kurtosis90.58208075
Mean0.06789074355
Median Absolute Deviation (MAD)0
Skewness8.205429887
Sum11185
Variance0.1191244987
2023-01-01T06:17:12.456721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 155872 94.6%
 
1 7904 4.8%
 
2 384 0.2%
 
4 317 0.2%
 
5 211 0.1%
 
3 61 < 0.1%
 
7 1 < 0.1%
 
ValueCountFrequency (%) 
0 155872 94.6%
 
1 7904 4.8%
 
2 384 0.2%
 
3 61 < 0.1%
 
4 317 0.2%
 
ValueCountFrequency (%) 
7 1 < 0.1%
 
5 211 0.1%
 
4 317 0.2%
 
3 61 < 0.1%
 
2 384 0.2%
 

total_runs
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS
Distinct count10
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3051289833080424
Minimum0
Maximum10
Zeros58061
Zeros (%)35.2%
Memory size1.3 MiB
2023-01-01T06:17:12.535961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.596254558
Coefficient of variation (CV)1.223062684
Kurtosis1.626415024
Mean1.305128983
Median Absolute Deviation (MAD)1
Skewness1.558685952
Sum215020
Variance2.548028613
2023-01-01T06:17:12.598917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 67672 41.1%
 
0 58061 35.2%
 
4 18914 11.5%
 
2 11696 7.1%
 
6 7360 4.5%
 
3 643 0.4%
 
5 333 0.2%
 
7 38 < 0.1%
 
8 25 < 0.1%
 
10 8 < 0.1%
 
ValueCountFrequency (%) 
0 58061 35.2%
 
1 67672 41.1%
 
2 11696 7.1%
 
3 643 0.4%
 
4 18914 11.5%
 
ValueCountFrequency (%) 
10 8 < 0.1%
 
8 25 < 0.1%
 
7 38 < 0.1%
 
6 7360 4.5%
 
5 333 0.2%
 

player_dismissed
Categorical

HIGH CARDINALITY
MISSING
Distinct count464
Unique (%)5.7%
Missing156593
Missing (%)95.0%
Memory size1.3 MiB
SK Raina
 
146
RV Uthappa
 
144
RG Sharma
 
141
G Gambhir
 
136
V Kohli
 
129
Other values (459)
7461
ValueCountFrequency (%) 
SK Raina 146 0.1%
 
RV Uthappa 144 0.1%
 
RG Sharma 141 0.1%
 
G Gambhir 136 0.1%
 
V Kohli 129 0.1%
 
KD Karthik 127 0.1%
 
S Dhawan 123 0.1%
 
PA Patel 112 0.1%
 
Yuvraj Singh 107 0.1%
 
YK Pathan 107 0.1%
 
Other values (454) 6885 4.2%
 
(Missing) 156593 95.0%
 
2023-01-01T06:17:12.689521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length20
Mean length3.316698027
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 26 48.1%
 
Uppercase_Letter 26 48.1%
 
Space_Separator 1 1.9%
 
Dash_Punctuation 1 1.9%
 
ValueCountFrequency (%) 
Latin 52 96.3%
 
Common 2 3.7%
 
ValueCountFrequency (%) 
ASCII 54 100.0%
 

dismissal_kind
Categorical

MISSING
Distinct count9
Unique (%)0.1%
Missing156593
Missing (%)95.0%
Memory size1.3 MiB
caught
4861
bowled
1495
run out
 
813
lbw
 
494
stumped
 
262
Other values (4)
 
232
ValueCountFrequency (%) 
caught 4861 3.0%
 
bowled 1495 0.9%
 
run out 813 0.5%
 
lbw 494 0.3%
 
stumped 262 0.2%
 
caught and bowled 211 0.1%
 
retired hurt 11 < 0.1%
 
hit wicket 9 < 0.1%
 
obstructing the field 1 < 0.1%
 
(Missing) 156593 95.0%
 
2023-01-01T06:17:12.789532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length21
Mean length3.160861912
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 20 95.2%
 
Space_Separator 1 4.8%
 
ValueCountFrequency (%) 
Latin 20 95.2%
 
Common 1 4.8%
 
ValueCountFrequency (%) 
ASCII 21 100.0%
 

fielder
Categorical

HIGH CARDINALITY
MISSING
Distinct count476
Unique (%)8.0%
Missing158832
Missing (%)96.4%
Memory size1.3 MiB
KD Karthik
 
145
MS Dhoni
 
142
RV Uthappa
 
120
AB de Villiers
 
108
SK Raina
 
107
Other values (471)
5296
ValueCountFrequency (%) 
KD Karthik 145 0.1%
 
MS Dhoni 142 0.1%
 
RV Uthappa 120 0.1%
 
AB de Villiers 108 0.1%
 
SK Raina 107 0.1%
 
PA Patel 88 0.1%
 
RG Sharma 87 0.1%
 
V Kohli 84 0.1%
 
NV Ojha 82 < 0.1%
 
WP Saha 78 < 0.1%
 
Other values (466) 4877 3.0%
 
(Missing) 158832 96.4%
 
2023-01-01T06:17:12.889146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length21
Mean length3.233414264
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 26 47.3%
 
Uppercase_Letter 25 45.5%
 
Space_Separator 1 1.8%
 
Open_Punctuation 1 1.8%
 
Close_Punctuation 1 1.8%
 
Dash_Punctuation 1 1.8%
 
ValueCountFrequency (%) 
Latin 51 92.7%
 
Common 4 7.3%
 
ValueCountFrequency (%) 
ASCII 55 100.0%
 

Interactions

2023-01-01T06:16:53.206722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:53.369171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:53.523462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:53.675001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:53.821128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:53.973865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:54.119022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:54.274245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:54.425926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:54.584395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:54.739342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:54.901636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:55.061296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:55.210357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:55.350021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:55.500317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:55.658057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:55.811505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:55.962264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:56.119975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:56.276441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:56.439987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:56.592699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:56.742902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:56.888639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:57.039892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:57.187802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:57.342863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:57.802829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:57.961833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:58.118536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:58.272056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:58.415415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:58.558367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:58.695515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:58.841902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:58.980543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:59.127099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:59.268878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:59.415128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:59.559748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:59.720918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:16:59.871151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:00.022767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:00.175242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:00.326615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:00.470817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:00.624916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:00.775174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:00.930619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:01.087454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:01.239058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:01.380599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:01.522587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:01.658136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:01.801763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:01.936788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:02.080134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:02.218581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:02.363946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:02.511673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:02.662668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:02.804253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:02.944278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:03.079825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:03.221405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:03.356422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:03.498837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:03.639219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:03.785790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:03.932626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:04.092919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:04.241383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:04.386644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:04.528814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:04.679082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:04.821391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:04.969884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:05.120044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:05.273822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:05.424807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:05.581612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:05.728571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:05.874470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:06.013856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:06.161404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:06.301375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:06.448840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:06.593595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:06.745051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:07.145061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:07.304109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:07.452473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:07.598358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:07.733715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:07.875956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:08.011834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:08.154245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:08.293126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:08.442481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-01T06:17:12.976679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-01T06:17:13.114597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-01T06:17:13.253865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-01T06:17:13.396937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-01-01T06:17:13.544920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-01-01T06:17:08.965783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:09.488599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:10.073655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-01T06:17:10.325883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
011Sunrisers HyderabadRoyal Challengers Bangalore11DA WarnerS DhawanTS Mills000000000NaNNaNNaN
111Sunrisers HyderabadRoyal Challengers Bangalore12DA WarnerS DhawanTS Mills000000000NaNNaNNaN
211Sunrisers HyderabadRoyal Challengers Bangalore13DA WarnerS DhawanTS Mills000000404NaNNaNNaN
311Sunrisers HyderabadRoyal Challengers Bangalore14DA WarnerS DhawanTS Mills000000000NaNNaNNaN
411Sunrisers HyderabadRoyal Challengers Bangalore15DA WarnerS DhawanTS Mills020000022NaNNaNNaN
511Sunrisers HyderabadRoyal Challengers Bangalore16S DhawanDA WarnerTS Mills000000000NaNNaNNaN
611Sunrisers HyderabadRoyal Challengers Bangalore17S DhawanDA WarnerTS Mills000100011NaNNaNNaN
711Sunrisers HyderabadRoyal Challengers Bangalore21S DhawanDA WarnerA Choudhary000000101NaNNaNNaN
811Sunrisers HyderabadRoyal Challengers Bangalore22DA WarnerS DhawanA Choudhary000000404NaNNaNNaN
911Sunrisers HyderabadRoyal Challengers Bangalore23DA WarnerS DhawanA Choudhary000010011NaNNaNNaN

Last rows

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
16474079532Chennai Super KingsSunrisers Hyderabad176SR WatsonAT RayuduRashid Khan000000404NaNNaNNaN
16474179532Chennai Super KingsSunrisers Hyderabad181AT RayuduSR WatsonS Kaul000000101NaNNaNNaN
16474279532Chennai Super KingsSunrisers Hyderabad182SR WatsonAT RayuduS Kaul000000101NaNNaNNaN
16474379532Chennai Super KingsSunrisers Hyderabad183AT RayuduSR WatsonS Kaul000000101NaNNaNNaN
16474479532Chennai Super KingsSunrisers Hyderabad184SR WatsonAT RayuduS Kaul000000404NaNNaNNaN
16474579532Chennai Super KingsSunrisers Hyderabad185SR WatsonAT RayuduS Kaul000000404NaNNaNNaN
16474679532Chennai Super KingsSunrisers Hyderabad186SR WatsonAT RayuduS Kaul000000000NaNNaNNaN
16474779532Chennai Super KingsSunrisers Hyderabad191AT RayuduSR WatsonCR Brathwaite000000000NaNNaNNaN
16474879532Chennai Super KingsSunrisers Hyderabad192AT RayuduSR WatsonCR Brathwaite000000000NaNNaNNaN
16474979532Chennai Super KingsSunrisers Hyderabad193AT RayuduSR WatsonCR Brathwaite000000404NaNNaNNaN